Assesment of vulnerability of a community to climate change requires a holistic understanding of the exposure, sensitivity and adaptive capacity of that particular community. The extant literature on the issue emphasizes importance of enhancing the adaptive capacity of households to improve their resilience to climate change. This study intends to analyze the factors that could define the adaptive capacity of the farming communities of Pakistan.Moreover, role of adaptive capacity in adaptation to climate change and then indirectly its influence on the yield of the major crops of Pakistan has also examined. The accomplishment of above mentioned goals entails a series of analyses.To identify the factors that influence households’ adaptive capacity, we constructed composite Household Adaptive Capacity Index (HACI). The index is constructed by employing assets based approach of Sustainable Livelihood Framework (SLF), where assets are categorized as human, physical, social, and natural capital. SLF views that availability of these assets enables people to respond to climate indicators and other stresses. This part of analysis helps to identify the factors needed to target in order to enhance the adaptive capacity of the households, hence to increase resilience of farmers to climate change. Given the fact that adaptive capacity of farm households functions as the driving force for adaptation, the analysis was further extended to study the adaptation decision and adaptation strategies undertaken. We study the impact of adaptive capacity on decision to adapt and, further examine thereof the expected increase in crop output when farmers are adapting to climate change. To evaluate the influence of different adaptation strategies—in isolation or in combination— on crops productivities, Multinomial Endogenous Switching (MES) Regression model is employed. This technique is adopted to account for the heterogeneity in the decision to adapt or not. Analysis to understand the impact of adaptation to climate change on yield was limited to four major crops of wheat, basmati rice, cotton, and sugarcane. It is asserted here that the analysis was controlled for socioeconomic and demographic characteristics shaping the adaptive capacity and adaptations of the farming community. The findings demonstrate a great deal of IX inequality in adaptive capacity in Pakistan especially among farm households of Sindh compared to Punjab and KPK. The findings further suggest that farmers should be assured with the provision of low-interest rate loans so that they could opt for off farm business, hence improve their livelihood portfolio and reduce dependence on farming. More resources should be deployed on the improvement of health status of farm households, quality of the houses, and provision of gas as a source of cooking. Social security should be provided to decrease the burden on bread winners from elderly and non-working members. Obstacles in effective linkages and collaboration with social groups and networks should be highlighted and removed. Gender should be taken seriously in climate change adaptation, with the provisions of rights and opportunities for the women in rural areas. In order to remove obstacles in farmers’ adaptation to climate change policies should be designed to increase the farm size, quality of extension centers and of the sources of information on climate change, and farmers’ should be facilitated with easy access to credit. Findings of the study further highlight that maximum gains in yield of wheat could be gained by introducing changes in irrigation practices. While, basmati rice producers could earn these gains by adapting the combination of input intensification and irrigation change. The evidence further suggest that cotton producers could receive maximum benefits from an adaptation by combining the change in time of sowing, input intensification and change in the irrigation practice. However, highest gains in the yield of sugarcane could be received when farmers adapt in the combination of input intensification, irrigation change, and varietal change
ملا طاہر سیف الدین گذشتہ دو مہینوں میں مسلمانوں کے دو بڑے قومی حادثے ہوئے، ۵؍ نومبر کو داؤدی بوہرون کے امام ملا طاہر سیف الدین نے انتقال کیا، ان کی ذات جامع صفات تھی، بڑے ذی علم، دیندار، فیاض و مخیر اور وسیع القلب تھے، دینی علوم پر ان کی نگاہ بہت وسیع تھی، اس لحاظ سے وہ ہندوستان کے ممتاز علماء میں تھے، صاحبِ قلم بھی تھے، عربی میں ان کی کئی تصانیف ہیں، انھوں نے اپنے دور میں نہ صرف اپنے فرقہ کی بڑی تعلیمی و اقتصادی خدمت کی بلکہ دوسرے اسلامی فرقوں کے ساتھ بھی ان کا سلوک روادرانہ و فیاضانہ تھا، اور ان کو ایک دوسرے کے قریب لانے کی کوشش کی، مسلم یونیورسٹی کے تو چانسلر ہی تھے، اس کو وقتاً فوقتاً بڑی بڑی رقمیں دیتے رہتے تھے، دارالمصنفین کی جوبلی کے موقع پر اس کو بارہ ہزار کا عطیہ دیا، اس لیے ہر فرقہ کے مسلمانوں میں عزت و وقعت کی نظر سے دیکھے جاتے تھے، اﷲ تعالیٰ ان کے حسنات کے طفیل میں ان کی مغفرت فرمائے، دارالمصنفین اس حادثہ میں ان کے لائق جانشین ملا برہان الدین کا شریک غم ہے اور دعا ہے کہ خدا ان کو ان کے باعظمت والد کے نقش قدم پر چلنے کی توفیق عطا فرمائے۔ (شاہ معین الدین ندوی، دسمبر ۱۹۶۵ء)
Islam is a natural and complete code of life for human being. Among the basic sources of Islamic teachings, The Holy Quran is the first and fundamental source. In the field of Islamic studies, Quran discipline has many branches and sub disciplines which assist us to understand the Quran, these sub-disciplines are called “Ulum Al Quran”. “Ulum Al Quran” are divided in hundreds types and Ilm Asbab Al Nuzul” is a very important among these types. The importance of this ilm can be gauged that the scholars and Interpreter of The Holy Quran have written separate books with respect of this ilm. We can see that after Allama Wahidi, Allama Jalaluddin Suyuti has also compiled a value able book named: "لباب النقول فی اسباب النزول"“Libab Al Nuqul Fi Asbab Al Nuzul” on this Ilm. In this article will try to find out and explain the methodology of Allama Jalaluddin Suyuti in his basic and majestic book with narration of its value ability, popularity and necessary introduction.
In view of the protection of Gaza coastal and marine environment from further deterioration and reverse the process from polluted to clean marine environment, it was necessary to analyse the seawater quality data and develop several artificial neural network (ANN) models to predict various water quality parameters. Hence, this study was undertaken with this objective. Even though there have been a few monitoring and evaluation literatures on the status of seawater pollution, however to the utmost of facts this work is the initial effort to use (ANN) technology for the prediction of seawater quality along Gaza coast. Additionally the land-based sources causing pollution along Gaza coastal waters have been assessed and also a management plan for Gaza coastal and marine environment protection proposed. All seawater quality monitoring data were inserted into Minitab statistical software and analysed by different tools including: min, max, mean, standard deviation and geometric mean. In addition, the Pearson correlation coefficient and paired sample t- test were used to perceive significant water quality differences at various sampling locations. The core objective of this work was to investigate whether it is possible to predict the next two weeks values of water quality parameters such as pH, electrical conductivity (EC), dissolved oxygen (DO), biological oxygen demand (BOD 5 ), Total kjeldahl nitrogen (TKN) and orthophosphate (Ortho-P). The data used was measured by several water quality monitoring programs in the Mediterranean Sea along Gaza. At the initial stage of the prediction of water quality variables, real water quality data along Gaza coast, over a period of four years beginning from 1997 to 2001, were collected from the Environment Quality Authority, the Environmental and Rural Research Centre-Islamic University of Gaza, Ministry of Health and some other organisations. The training and testing of the developed ANN assessment models was carried out using neural network toolbox in the MATLAB. Two types of feedforward networks have been used. They are Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks. xxiFour different MLP neural networks have been trained and developed with reference to water temperature, wind velocity and turbidity parameters to predict pH and EC fortnight’s values. MLP and RBF neural networks have been used for predicting the next fortnight’s dissolved oxygen concentrations. Both networks are trained and developed with reference to the five oceanographic variables including water temperature, wind velocity, turbidity, pH and conductivity. MLP neural network has also been trained to predict BOD 5 level. MLP and RBF neural networks have been trained with five parameters to predict nutrients (TKN and Ortho-P) level along Gaza coast. Prediction results prove that both types of networks are highly satisfactory for predicting water quality parameters in the Mediterranean Sea along Gaza coast. Results of the developed networks have also been compared with the statistical model and found that ANN predictions are better than the conventional methods. The human activities including: urbanisation in coastal areas, wastewater pollution, coastal activities, agricultural loads, industrial pollution, influence of fisheries and solid waste as well as debris exert pressure on the marine and coastal environment. All these are land-based sources causing pollution along Gaza coastal environment. In order to reduce the load of pollution in the coastal waters of Gaza, it is recommended that the Palestinian Environment Quality Authority (EQA) shall play a major and active role in implementing the Gaza coastal environmental management plan (GCEMP). Therefore, EQA shall prepare the short term and long term action plans through coordination with all the relevant stakeholders. It is hoped that ANN developed models and the proposed management plan will help in assisting the local authorities in developing plans that are necessary to minimise the sources that cause pollution along Gaza coastal waters.